{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X token train shape: (10244,)\n",
      "X token test shape: (2560,)\n",
      "X morph train shape: (10244,)\n",
      "X morph test shape: (2560,)\n"
     ]
    }
   ],
   "source": [
    "import codecs\n",
    "import re\n",
    "from keras.utils.np_utils import to_categorical\n",
    "import numpy as np\n",
    "\n",
    "def load_data(filename):\n",
    "    data = list(codecs.open(filename, 'r', 'utf-8').readlines())\n",
    "    x, y = zip(*[d.strip().split('\\t') for d in data])\n",
    "    # Reducing any char-acter sequence of more than 3 consecutive repetitions to a respective 3-character sequence \n",
    "    # (e.g. “!!!!!!!!”turns to “!!!”)\n",
    "    # x = [re.sub(r'((.)\\2{3,})', r'\\2\\2\\2', i) for i in x]\n",
    "    x = np.asarray(list(x))\n",
    "    y = to_categorical(y, 3)\n",
    "    \n",
    "    return x, y\n",
    "    \n",
    "x_token_train, y_token_train = load_data('data/token_train.tsv')\n",
    "x_token_test, y_token_test = load_data('data/token_test.tsv')\n",
    "x_morph_train, y_morph_train = load_data('data/morph_train.tsv')\n",
    "x_morph_test, y_morph_test = load_data('data/morph_test.tsv')\n",
    "\n",
    "print('X token train shape: {}'.format(x_token_train.shape))\n",
    "print('X token test shape: {}'.format(x_token_test.shape))\n",
    "\n",
    "print('X morph train shape: {}'.format(x_morph_train.shape))\n",
    "print('X morph test shape: {}'.format(x_morph_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ממש כואב ..... אני בוכה עם המשפחה שלא תדעו עוד צער' 'איש יקר שלנו'\n",
      " 'כל הכבוד והמון בהצלחה'\n",
      " '\" תל חי , רובי . בכל העצב הזה היית קרן אור של תקוה . אכן יש נשיא בישראל \"'\n",
      " 'נקי כפיים ובר לבב בהצלחה לך ולנו .']\n"
     ]
    }
   ],
   "source": [
    "print(x_token_train[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['\" שמע ישראל , השם ישמור ויקרא הגורל = ( י.ק.ו.ק . ) אימרו אמן לאבא השם שלנו ! ! ! ! אחרי ברכה של ביבי ! הכח בישראל הוא מתי שיש משמעת ופרגמתיות במשרדי החינוך שזה איתן את האור ! שמאוד חסר לנו ! , והתאחדות באחד שלם , ואין שמאל ואין ימין ! ובישראל נקודה חשובה היא , תעשיית כוח פרגמטיבית ! https://www.youtube.com/watch?v=_rKMXgPQSj8 . עוד מעת אהיה ראש חודש תעברו על ה תפילה של התיקון הכללי ו תדליקו את הנר ! \"'\n",
      " 'איחולי הצלחה בתפקידך .' 'כל הכבוד !!!'\n",
      " '\" בוקר טוב ישראל בוקר טוב לכבוד נשיא מדינת ישראל . ״ אשרי העם שנבחר אדם עשיר בענווה , יושרה ודעת ״ מי ייתן ותאחד את עמך ישראל . יישר כוח . עופר אלפסי מאילת . \"'\n",
      " 'איפה הגינוי ? http://www.iba.org.il/bet/bet.aspx?type=1&entity=1023105']\n"
     ]
    }
   ],
   "source": [
    "print(x_token_test[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ממש כואב ..... אני בכה את היא עם ה משפחה ש לא תדעו עוד צער'\n",
      " 'איש יקר של אנחנו' 'כל ה כבוד ו המון ב הצלחה'\n",
      " '\" תל חי , רובי . ב כל ה עצב ה זה היית קרן אור של תקוה . אכן יש נשיא ב ישראל \"'\n",
      " 'נקי כפיים ו בר לבב ב הצלחה ל אתה ו ל אנחנו .']\n"
     ]
    }
   ],
   "source": [
    "print(x_morph_train[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['\" שמע ישראל , ה שם ישמור ו יקרא ה גורל =  ( י.ק.ו.ק . ) אימרו אמן ל אבא ה שם של אנחנו ! ! ! ! אחרי ברכה של ביבי ! ה כח ב ה ישראל הוא מתי ש יש משמעת ו פרגמתיות ב משרדי ה חינוך ש זה איתן את ה אור ! ש מאוד חסר ל אנחנו ! , ו התאחדות ב אחד שלם , ו אין שמאל ו אין ימין ! ו ב ישראל נקודה חשובה היא , תעשיית כוח פרגמטיבית ! https://www.youtube.com/watch?v=_rKMXgPQSj8 . עוד מעת אהיה ראש חודש תעברו על ה תפילה של ה תיקון ה כללי ו תדליקו את ה נר ! \"'\n",
      " 'איחולי הצלחה ב תפקידך .' 'כל ה כבוד !!!'\n",
      " '\" בוקר טוב ישראל בוקר טוב לכבוד נשיא מדינת ישראל . ״ אשרי ה עם ש נבחר אדם עשיר ב ענווה , יושרה ו דעת ״  מי ייתן ו תאחד את עמך ישראל . יישר כוח . עופר אלפסי מאילת . \"'\n",
      " 'איפה ה גינוי ? http://www.iba.org.il/bet/bet.aspx?type=1&entity=1023105']\n"
     ]
    }
   ],
   "source": [
    "print(x_morph_test[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare\n",
    "Convert text (train & test) to sequences and pad to requested document length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X token train shape: (10244, 100)\n",
      "X token test shape: (2560, 100)\n",
      "X morph train shape: (10244, 100)\n",
      "X morph test shape: (2560, 100)\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing import text, sequence\n",
    "\n",
    "def tokenizer(x_train, x_test, vocabulary_size, char_level):\n",
    "    tokenize = text.Tokenizer(num_words=vocabulary_size, \n",
    "                              char_level=char_level,\n",
    "                              filters='')\n",
    "    tokenize.fit_on_texts(x_train)  # only fit on train\n",
    "    #print('UNK index: {}'.format(tokenize.word_index['UNK']))\n",
    "    \n",
    "    x_train = tokenize.texts_to_sequences(x_train)\n",
    "    x_test = tokenize.texts_to_sequences(x_test)\n",
    "    \n",
    "    return x_train, x_test\n",
    "\n",
    "def pad(x_train, x_test, max_document_length):\n",
    "    x_train = sequence.pad_sequences(x_train, maxlen=max_document_length, padding='post', truncating='post')\n",
    "    x_test = sequence.pad_sequences(x_test, maxlen=max_document_length, padding='post', truncating='post')\n",
    "    \n",
    "    return x_train, x_test\n",
    "\n",
    "vocabulary_size = 5000\n",
    "\n",
    "x_token_train, x_token_test = tokenizer(x_token_train, x_token_test, vocabulary_size, False)\n",
    "x_morph_train, x_morph_test = tokenizer(x_morph_train, x_morph_test, vocabulary_size, False)\n",
    "\n",
    "max_document_length = 100\n",
    "\n",
    "x_token_train, x_token_test = pad(x_token_train, x_token_test, max_document_length)\n",
    "x_morph_train, x_morph_test = pad(x_morph_train, x_morph_test, max_document_length)\n",
    "\n",
    "print('X token train shape: {}'.format(x_token_train.shape))\n",
    "print('X token test shape: {}'.format(x_token_test.shape))\n",
    "\n",
    "print('X morph train shape: {}'.format(x_morph_train.shape))\n",
    "print('X morph test shape: {}'.format(x_morph_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Token OOV ratio: 0.08865112724493696 (2552 out of 28787)\n",
      "Morph OOV ratio: 0.07624788494077835 (1442 out of 18912)\n"
     ]
    }
   ],
   "source": [
    "print('Token OOV ratio: {} ({} out of 28787)'.format(np.count_nonzero(x_token_test == 28787)/28787, np.count_nonzero(x_token_test == 28787)))\n",
    "print('Morph OOV ratio: {} ({} out of 18912)'.format(np.count_nonzero(x_morph_test == 18912)/18912, np.count_nonzero(x_morph_test == 18912)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_loss_and_accuracy(history):\n",
    "    \n",
    "    fig, axs = plt.subplots(1, 2, sharex=True)\n",
    "    \n",
    "    axs[0].plot(history.history['loss'])\n",
    "    axs[0].plot(history.history['val_loss'])\n",
    "    axs[0].set_title('Model Loss')\n",
    "    axs[0].legend(['Train', 'Validation'], loc='upper left')\n",
    "    \n",
    "    axs[1].plot(history.history['acc'])\n",
    "    axs[1].plot(history.history['val_acc'])\n",
    "    axs[1].set_title('Model Accuracy')\n",
    "    axs[1].legend(['Train', 'Validation'], loc='upper left')\n",
    "    \n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import required modules from Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten, Input, Concatenate\n",
    "from keras.layers import Embedding\n",
    "from keras.layers import LSTM, Bidirectional\n",
    "from keras.layers.convolutional import Conv1D\n",
    "from keras.layers.pooling import MaxPool1D\n",
    "from keras.layers import BatchNormalization\n",
    "from keras import optimizers\n",
    "from keras import metrics\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Default Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "dropout_keep_prob = 0.5\n",
    "embedding_size = 300\n",
    "batch_size = 50\n",
    "lr = 1e-4\n",
    "dev_size = 0.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear - Token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/10\n",
      "8195/8195 [==============================] - 1s 102us/step - loss: 9.4109 - acc: 0.3852 - val_loss: 8.0918 - val_acc: 0.4700\n",
      "Epoch 2/10\n",
      "8195/8195 [==============================] - 0s 56us/step - loss: 7.4096 - acc: 0.5154 - val_loss: 6.4014 - val_acc: 0.5930\n",
      "Epoch 3/10\n",
      "8195/8195 [==============================] - 0s 61us/step - loss: 6.3387 - acc: 0.5957 - val_loss: 6.0046 - val_acc: 0.6203\n",
      "Epoch 4/10\n",
      "8195/8195 [==============================] - 0s 58us/step - loss: 5.9950 - acc: 0.6190 - val_loss: 5.9001 - val_acc: 0.6276\n",
      "Epoch 5/10\n",
      "8195/8195 [==============================] - 0s 54us/step - loss: 5.6493 - acc: 0.6411 - val_loss: 5.5351 - val_acc: 0.6506\n",
      "Epoch 6/10\n",
      "8195/8195 [==============================] - 1s 77us/step - loss: 5.4348 - acc: 0.6515 - val_loss: 5.5007 - val_acc: 0.6530\n",
      "Epoch 7/10\n",
      "8195/8195 [==============================] - 0s 54us/step - loss: 5.3617 - acc: 0.6582 - val_loss: 5.3959 - val_acc: 0.6613\n",
      "Epoch 8/10\n",
      "8195/8195 [==============================] - 0s 58us/step - loss: 5.1730 - acc: 0.6681 - val_loss: 5.1089 - val_acc: 0.6750\n",
      "Epoch 9/10\n",
      "8195/8195 [==============================] - 1s 69us/step - loss: 5.0766 - acc: 0.6747 - val_loss: 5.1133 - val_acc: 0.6764\n",
      "Epoch 10/10\n",
      "8195/8195 [==============================] - 1s 68us/step - loss: 5.0019 - acc: 0.6816 - val_loss: 5.0307 - val_acc: 0.6833\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c088a9e9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 0s 27us/step\n",
      "\n",
      "Accurancy: 0.6820\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 10\n",
    "\n",
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Dense(100)(text_input)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_token_train, y_token_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_token_test, y_token_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.4f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('word_saved_models/Linear-Token-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear - Morph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/10\n",
      "8195/8195 [==============================] - 1s 97us/step - loss: 9.8982 - acc: 0.3519 - val_loss: 8.2902 - val_acc: 0.4573\n",
      "Epoch 2/10\n",
      "8195/8195 [==============================] - 0s 52us/step - loss: 7.7296 - acc: 0.5024 - val_loss: 7.0930 - val_acc: 0.5451\n",
      "Epoch 3/10\n",
      "8195/8195 [==============================] - 0s 60us/step - loss: 6.5224 - acc: 0.5832 - val_loss: 6.4932 - val_acc: 0.5900\n",
      "Epoch 4/10\n",
      "8195/8195 [==============================] - 0s 60us/step - loss: 6.1808 - acc: 0.6079 - val_loss: 6.1911 - val_acc: 0.6091\n",
      "Epoch 5/10\n",
      "8195/8195 [==============================] - 0s 61us/step - loss: 5.8964 - acc: 0.6281 - val_loss: 6.0357 - val_acc: 0.6223\n",
      "Epoch 6/10\n",
      "8195/8195 [==============================] - 0s 58us/step - loss: 5.7071 - acc: 0.6399 - val_loss: 5.9109 - val_acc: 0.6276\n",
      "Epoch 7/10\n",
      "8195/8195 [==============================] - 0s 59us/step - loss: 5.6071 - acc: 0.6470 - val_loss: 5.8021 - val_acc: 0.6374\n",
      "Epoch 8/10\n",
      "8195/8195 [==============================] - 0s 57us/step - loss: 5.5011 - acc: 0.6555 - val_loss: 5.6980 - val_acc: 0.6442\n",
      "Epoch 9/10\n",
      "8195/8195 [==============================] - 1s 67us/step - loss: 5.4568 - acc: 0.6585 - val_loss: 5.6213 - val_acc: 0.6476\n",
      "Epoch 10/10\n",
      "8195/8195 [==============================] - 1s 65us/step - loss: 5.4034 - acc: 0.6616 - val_loss: 5.5811 - val_acc: 0.6506\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c0846ce0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 0s 20us/step\n",
      "\n",
      "Accurancy: 0.6613\n"
     ]
    }
   ],
   "source": [
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Dense(100)(text_input)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_morph_train, y_morph_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_morph_test, y_morph_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.4f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('word_saved_models/Linear-Morph-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN - Token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/5\n",
      "8195/8195 [==============================] - 111s 14ms/step - loss: 0.6998 - acc: 0.6997 - val_loss: 0.6503 - val_acc: 0.7291\n",
      "Epoch 2/5\n",
      "8195/8195 [==============================] - 108s 13ms/step - loss: 0.5681 - acc: 0.7799 - val_loss: 0.4971 - val_acc: 0.8072\n",
      "Epoch 3/5\n",
      "8195/8195 [==============================] - 103s 13ms/step - loss: 0.4006 - acc: 0.8614 - val_loss: 0.4010 - val_acc: 0.8619\n",
      "Epoch 4/5\n",
      "8195/8195 [==============================] - 103s 13ms/step - loss: 0.2916 - acc: 0.9042 - val_loss: 0.3629 - val_acc: 0.8751\n",
      "Epoch 5/5\n",
      "8195/8195 [==============================] - 108s 13ms/step - loss: 0.2275 - acc: 0.9302 - val_loss: 0.3543 - val_acc: 0.8834\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c08419b240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 9s 4ms/step\n",
      "\n",
      "Accurancy: 0.892\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 5\n",
    "\n",
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "convs = []\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "for fsz in [3, 8]:\n",
    "    conv = Conv1D(128, fsz, padding='valid', activation='relu')(x)\n",
    "    pool = MaxPool1D()(conv)\n",
    "    convs.append(pool)\n",
    "x = Concatenate(axis=1)(convs)\n",
    "x = Flatten()(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_token_train, y_token_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_token_test, y_token_test,\n",
    "                        batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.3f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('word_saved_models/CNN-Token-{:.3f}.h5'.format((scores[1] * 100)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN - Morph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/5\n",
      "8195/8195 [==============================] - 121s 15ms/step - loss: 0.7027 - acc: 0.7024 - val_loss: 0.6580 - val_acc: 0.7174\n",
      "Epoch 2/5\n",
      "8195/8195 [==============================] - 111s 14ms/step - loss: 0.5823 - acc: 0.7684 - val_loss: 0.4978 - val_acc: 0.8233\n",
      "Epoch 3/5\n",
      "8195/8195 [==============================] - 109s 13ms/step - loss: 0.4035 - acc: 0.8576 - val_loss: 0.3968 - val_acc: 0.8536\n",
      "Epoch 4/5\n",
      "8195/8195 [==============================] - 111s 14ms/step - loss: 0.2978 - acc: 0.9019 - val_loss: 0.3528 - val_acc: 0.8829\n",
      "Epoch 5/5\n",
      "8195/8195 [==============================] - 114s 14ms/step - loss: 0.2364 - acc: 0.9259 - val_loss: 0.3397 - val_acc: 0.8853\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c085dd0240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 8s 3ms/step\n",
      "\n",
      "Accurancy: 0.8906\n"
     ]
    }
   ],
   "source": [
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "convs = []\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "for fsz in [3, 8]:\n",
    "    conv = Conv1D(128, fsz, padding='valid', activation='relu')(x)\n",
    "    pool = MaxPool1D()(conv)\n",
    "    convs.append(pool)\n",
    "x = Concatenate(axis=1)(convs)\n",
    "x = Flatten()(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_morph_train, y_morph_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_morph_test, y_morph_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.4f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('word_saved_models/CNN-Morph-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM - Token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/7\n",
      "8195/8195 [==============================] - 153s 19ms/step - loss: 0.8279 - acc: 0.6556 - val_loss: 0.7428 - val_acc: 0.6593\n",
      "Epoch 2/7\n",
      "8195/8195 [==============================] - 156s 19ms/step - loss: 0.7493 - acc: 0.6649 - val_loss: 0.7407 - val_acc: 0.6623\n",
      "Epoch 3/7\n",
      "8195/8195 [==============================] - 151s 18ms/step - loss: 0.7424 - acc: 0.6709 - val_loss: 0.7399 - val_acc: 0.6686\n",
      "Epoch 4/7\n",
      "8195/8195 [==============================] - 155s 19ms/step - loss: 0.7361 - acc: 0.6746 - val_loss: 0.7372 - val_acc: 0.6803\n",
      "Epoch 5/7\n",
      "8195/8195 [==============================] - 147s 18ms/step - loss: 0.7372 - acc: 0.6776 - val_loss: 0.7327 - val_acc: 0.6823\n",
      "Epoch 6/7\n",
      "8195/8195 [==============================] - 148s 18ms/step - loss: 0.6492 - acc: 0.7402 - val_loss: 0.6796 - val_acc: 0.6906\n",
      "Epoch 7/7\n",
      "8195/8195 [==============================] - 148s 18ms/step - loss: 0.4769 - acc: 0.8409 - val_loss: 0.4786 - val_acc: 0.8399\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c09b241a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 12s 5ms/step\n",
      "\n",
      "Accurancy: 0.852\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 7\n",
    "lstm_units = 93\n",
    "\n",
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "x = LSTM(units=lstm_units, return_sequences=True)(x)\n",
    "x = LSTM(units=lstm_units)(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_token_train, y_token_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_token_test, y_token_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.3f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('word_saved_models/LSTM-Token-{:.3f}.h5'.format((scores[1] * 100)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM - Morph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/7\n",
      "8195/8195 [==============================] - 99s 12ms/step - loss: 0.8054 - acc: 0.6630 - val_loss: 0.7406 - val_acc: 0.6593\n",
      "Epoch 2/7\n",
      "8195/8195 [==============================] - 100s 12ms/step - loss: 0.7322 - acc: 0.6671 - val_loss: 0.7363 - val_acc: 0.6593\n",
      "Epoch 3/7\n",
      "8195/8195 [==============================] - 99s 12ms/step - loss: 0.7283 - acc: 0.6748 - val_loss: 0.7326 - val_acc: 0.6911\n",
      "Epoch 4/7\n",
      "8195/8195 [==============================] - 96s 12ms/step - loss: 0.7148 - acc: 0.6941 - val_loss: 0.7277 - val_acc: 0.6984\n",
      "Epoch 5/7\n",
      "8195/8195 [==============================] - 96s 12ms/step - loss: 0.5452 - acc: 0.7980 - val_loss: 0.4492 - val_acc: 0.8277\n",
      "Epoch 6/7\n",
      "8195/8195 [==============================] - 99s 12ms/step - loss: 0.3767 - acc: 0.8709 - val_loss: 0.4376 - val_acc: 0.8375\n",
      "Epoch 7/7\n",
      "8195/8195 [==============================] - 96s 12ms/step - loss: 0.3036 - acc: 0.9054 - val_loss: 0.4061 - val_acc: 0.8619\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c08f6fb208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 8s 3ms/step\n",
      "\n",
      "Accurancy: 0.862\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 7\n",
    "\n",
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "x = LSTM(units=lstm_units, return_sequences=True)(x)\n",
    "x = LSTM(units=lstm_units)(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_morph_train, y_morph_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_morph_test, y_morph_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.3f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('word_saved_models/LSTM-Morph-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BiLSTM - Token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/3\n",
      "8195/8195 [==============================] - 219s 27ms/step - loss: 0.7574 - acc: 0.6876 - val_loss: 0.6579 - val_acc: 0.7272\n",
      "Epoch 2/3\n",
      "8195/8195 [==============================] - 212s 26ms/step - loss: 0.5360 - acc: 0.7993 - val_loss: 0.4653 - val_acc: 0.8253\n",
      "Epoch 3/3\n",
      "8195/8195 [==============================] - 212s 26ms/step - loss: 0.3556 - acc: 0.8819 - val_loss: 0.4067 - val_acc: 0.8536\n"
     ]
    },
    {
     "data": {
      "image/png": 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37dr1nAOCU6dO5YEHHjhvm0qVjHMBKSkpDBky5LxyY2NjL9j31KlTycjIOPv6+uuvJz09/QItNBr3R4+pssEvWw/zyrJt9I2sy0PXNHVq3x5loESE+tUCKVBwMD2z5AYXwbBhw5g9e/Z/ymbPns2wYcNKbFuvXj3mzp17yX0XHUxLliwhKCjokuVpNO6AHlPuz47Dp3hk1noi6lXltSFtnb514lEGCmzDIOVyMtO6pb4hQ4bwww8/kJ1teAru2bOHlJQUoqKi6N69O9HR0bRp04aFCxee03bPnj1EREQAkJmZydChQ4mMjOTmm28mM/P/DemoUaPOphSYMGECAO+88w4pKSl069aNbt26ARAaGsrRo0cBePPNN4mIiCAiIoKpU6ee7a9Vq1aMGDGC1q1b07Nnz//0o9G4A3pMuTfHz+Rw7+exVPD3Yfrt7Qn0c77zWdlzklg6Fg5tvmCVWigq5xjRzpWfN1LseUkb6rSB3lMuWKVGjRp06NCBZcuWMWDAAGbPns3NN99MYGAg8+fPp0qVKhw9epQrrriC/v37n/eXxgcffECFChXYtGkTmzZtIjo6+uy1yZMnU716dfLz8+nevTubNm3ikUce4c0332TlypUEBwf/R1ZcXBwzZ85kzZo1KKW4/PLLufrqq6lWrRo7d+5k1qxZzJgxg5tuuonvv/+eW2+99cLvg6b8Yse4umhKGFd6TLkvufkFjPo6jkMns/h25BXUrRroEj08bgYFIAj+vt4oBTl51u1F2S5JFC5FKKV4+umniYyMpEePHhw4cIDDhw+fV8Yff/xx9kMdGRlJZGTk2Wtz5swhOjqadu3aER8fX2xKAVv+/PNPBg0aRMWKFalUqRKDBw9m1apVAISFhREVZaTh0qkHNO6KHlPuyfOL4vkn6Riv3NCGdiHVXKZH2ZtBlTDTKcQbOJGeSdrpbJrWrEQF/9Lf6sCBAxkzZgzr1q0jMzOT6OhoPvvsM1JTU4mLi8PX15fQ0NBiUwHYUtwvwd27d/P666+zdu1aqlWrxp133lminP9Glfov/v7/f0bB29vb45cjNKXEznFlNXpMuR9frt7D12v2cf/VTRjUroFLdfHIGVQhdar44+vtRXJ6JgUWpIivVKkSXbt25e677z67kXvixAlq1aqFr68vK1euZO/evReUcdVVV/H1118DsGXLFjZt2gQYKQUqVqxI1apVOXz4MEuXLj3bpnLlypw6dapYWQsWLCAjI4MzZ84wf/58unTpUur71GichR5T7sXfiUd5fnEC3VvW4snrWrhanTI4g7oIvL28qB8UyJ60Mxw9lU2tKgGlljls2DAGDx58dlli+PDh9OvXj5iYGKKiomjZsuUF248aNYq77rqLyMhIoqKi6NChAwBt27alXbt2tG7d+pyUAiNHjqR3797UrVuXlStXni2Pjo7mzjvvPCvj3nvvpV27dh699KDxPPSYcg/2HD3DqK/X0aRmRaYOjXKLFEZyoSmtK4iJiVFFzzBs3bqVVq0uPY3w3rQznMzKo3mtSjoMUhFK+956OiISp4yEm2UaR4wrzfkpa+/tyaxcBr//N2mns1n4YGeHZyq3d1x59BJfIfWCAvESSE7PvOAas0aj0ZQ38gsUo2etZ8/RM7w/vL3DjdPFUC4MlK+3F3WqBnAmO4/jGTmuVkej0WjchleXbWPl9lSe79+ajk1quFqd/1BmDFRpZz7VK/hR0d+HgyeyLA+DVFbRs0mN/gxYT1l6T+fGJfPRH0nc3rERt17RyNXqnEOZMFABAQGkpaWV6h8vItQPMsIgpVgcBqksopQiLS2NgIDSO45oyiZWjCvNfylL4ypu73GenreZK5vU4Nm+4a5Wp1js8uITkV7A2xjHiz5WSk0pcv0toJv5sgJQSykVZF67AxhvXpuklPr8YpVs0KABycnJWJG2OiMrl0OZeRyr5EdAOXeYCAgIoEED155z0LgOK8eV5v8pC+MqJT2T+76Mo25QAO8Pj8bX2z3nKiUaKBHxBqYB12KksF4rIovMPDYAKKUes6n/MNDOfF4dmADEYCRjizPbHr8YJX19fQkLC7uYJuclJ6+Afu/+ycmsXFY8dhWVA3wtkavRlDWsHFeaskNGTh4jvoglKzefWSMuJ6iCn6tVOi/2mM0OQKJSKkkplYORfnrABeoPA2aZz68DflJKHTON0k9Ar9IoXFr8fLx4+YY2HDqZxevLt7tSFY1Go3EqSime/G4TCQdP8u6wdjSrXdlRHUFB6ff67TFQ9YH9Nq+TzbJzEJFGQBjw68W0FZGRIhIrIrHOWG6IDqnG7Vc04ot/9hK396ImcxqNRlNmeeeXRH7cfJBxvVvSrWUt6zvIOgFrPoJpHSDxp1KLs8dAFXec+Hy7qkOBuUqp/Itpq5SarpSKUUrF1KxZ0w6VSs+TvVpSp0oA4+ZtsjSgrEaj0bgjSzcf5K2fdzA4uj4jujS2VvihzbB4NLzREpb+D/wrg3fplw7tMVDJQEOb1w2AlPPUHcr/L+9dbFunUsnfh0kDI9hx+DQf/b7L1epoPBQR6SUi20UkUUTGFnM9RERWish6EdkkIteb5aEikikiG8zHhzZt2ovIZlPmO+LsLHKaMkd8ygnGzNlIdEgQLw1qY03iwbxs2PQdfHIdfNgZNs6GiMEwYiWM+BWadCtZRgnY48W3FmgmImHAAQwjdEvRSiLSAqgGrLYpXg68JCKF8dp7AuNKpbGFdG9Vmz6RdXn310Suj6xLk5qVXK2SxoOwx8EIw8N1jlLqAxEJB5YAoea1XUqpqGJEfwCMBP4x6/cClhZTT6Mh9VQ2Iz6PJaiCLx/e1r703svp+yFuJqz7As6kQvXG0HMyRN0CFapbo7RJiQZKKZUnIg9hGBtv4FOlVLyITARilVKLzKrDgNnK5lCFUuqYiLyIYeQAJiqljll6B6VkQr9wVu1IZdy8zcwecQVebhAgUeMxnHUwAhCRQgcjWwOlgCrm86qUsMIgInWBKkqp1ebrL4CBaAOlKYbsvHzu+zKWYxk5zL3/SmpVvsTzWQUFkPQrrP0Ediwzypr3gsvuhcbdwMsxbup2nYNSSi3B+KVmW/ZckdfPn6ftp8Cnl6ifw6lVOYBn+rTiqe83823sfoZ1CHG1ShrPoTgnocuL1HkeWGEez6gI9LC5FiYi64GTwHil1CpTZnIRmedzWhqJMdMiJER/rssbSimemb+FdfvSmXZLNBH1q168kIxjsOEbiP0EjiVBhWDo/Bi0vxOCHP+Z8uh0G/ZyU0xD5q8/wEtLttK9ZS1L0nJoNNjnJDQM+Ewp9YaIdAS+FJEI4CAQopRKE5H2wAIRaW2nTKNQqenAdDCimV/qTWjKJp/8uZu5ccmM7t6MPpF1L65xynpY+zFsngt5WdDwCuj6NIT3Bx//kttbhDZQGGGQXh4cyXVT/+D5xfG8P7y9q1XSeAb2OAndg3k2UCm1WkQCgGCl1BEg2yyPE5FdQHNTpm2YArdxPNK4Dyu3H+GlJVvpHVGH0d2b2dcoNwvi5xmG6UAc+FaAtkONZbw6bRyr8Hlwz/gWLiAsuCKjuzdjyeZDrIg/5Gp1NJ7BWQcjEfHDcDBaVKTOPqA7gIi0AgKAVBGpaTpZICKNgWZAklLqIHBKRK4wvfduBxY653Y0ZYHEI6d45Jv1tKxThTdualvyvvqx3bDiWXizFSwYBdmnoPer8Pg26Pe2y4wT6BnUfxh5VWMWb0zhuYXxdGxSQ4dB0pQKOx2MHgdmiMhjGEt1dyqllIhcBUwUkTwgH7jfxsFoFPAZEIjhHKEdJDQApGfkcO/nsfj7ejHjjhgq+J3nK74gHxJ/hn9nGH/FC1r2MWZLYVeBm5xc0AbKBl9vL14e3IbBH/zNq8u28+LACFerpCnjlORgZLqcdyqm3ffA9+eRGQvoD6fmP+TmF/DgN+tISc9i1sjLqR8UeG6lM2mw/guI/RTS90GlOnD1U9D+DqhSz/lKl4A2UEVoF1KNOzqG8vnqPQxsV4/2jaz169doNBpHMOmHBP5KTOO1IZH//d5SCpJjjb2l+PmQnw2hXeDaidCyL3i770qRNlDF8MR1LVgRf4ix32/mh0c64+9TvtNyaDQa9+brNXv5fPVeRnQJ48YY0y8nJwO2zDWW8Q5tAr/KEH07XHYP1GrlWoXtRBuoYqjk78OkQRHc/VksH/6WxOgednrBaDQajZNZvSuNCQvj6dqiJmN7t4Kjica5pQ1fG8Fba4VDnzch8iYjRl4ZQhuo83BNy9r0jazLtJWJ9ImsQ9NaZesfq9FoPJ99aRk88HUcYdX9eb/9Qby/GgRJK8HLB8IHGE4PIR3dxunhYtEG6gJM6NeaVTuPMm7eZr4d2VGHQdJoNG7DqaxcnvhsBXcXLGUUf+AzLwWq1Idu442lvMq1Xa1iqdEG6gLUrOzPM9e34n/fb2LW2n0Mv7yRq1XSaDTlHaXI3/M3CXNf46vTf+An+VCrG/R5zYiP5+05X+uecycO4saYBizYcIApS7bRo1VtauswSBqNxhVkn4JNc2DtJ3gfiaeVqkBio2GE938Ugj1zn1xHkigBEeGlQW3IyS9gwsJ4V6uj0WjKG0e2wZIn4Y1W8OMY0rML+F/uCN5os5BWd73nscYJ9AzKLkKDKzK6RzNeXbad5fGHuK51HVerpNFoPJn8XNj2g5HeYs8qIztt68FsD7mJfguyaRdSja8GxViTeNCN0QbKTkZ0aczijQd5buEWOjapQRUdBkmj0VjNyRSI+xziPoPTh4yUFj2eh3a3cSivEre99ye1qwTwwa3t8fX2/AWwsnWHabuM8BwuwNfbiymD25B6KptXl21ziQ4ajcYDUQp2/wHf3gZvRcDvrxgBWm+ZA49sgM6PkelbjZFfxnImO4+Pb7+M6hX9XK21Uyg7M6i8HPi8n+FGedcSl4TnaNswiDuvDOPTv3YzMKo+MaE6DJJGo7lEsk7AxtlGCKKjOyCwGnR8EGLuMtKomyil+N/3m9h84AQzbouhRZ3ycyaz7MygfPyg54uQ/C/89rLL1Hi8Z3PqBwXy1PebyM7Ld5keGo2mjHJoCyx+1HB6WPo/I7rDwA9gzFbjO87GOAFMW5nI4o0pPHldC3qEl/2zTReDXQZKRHqJyHYRSRSRseepc5OIJIhIvIh8Y1OeLyIbzEfRXDgXR8QNxgG0VW9C0m+lEnWpVDTDIO1KPcP7K3e5RAeNRlPGyMs2stN+ch182Ak2zoKIQTBiJYz4FaJuAd9zo48v23KI11fsYGBUPUZd3cQFiruWEpf4zKRp04BrMbJ5rhWRRWaagMI6zYBxQCel1HERqWUjIlMpFWWZxr1egX1rYN5IuP8vqFTTMtH20q1FLfq3rcf7vyXSN7IuzWqXnym3RqO5CNL3Q9xMWPcFnEk1Zkc9JxsGqcKFtwi2HjzJmDkbaNswiCk3RHq8x15x2DOD6gAkKqWSlFI5wGxgQJE6I4BpSqnjAGa6asfgVwFunAmZ6bDgfigocFhXF+K5fuFU9Pdh7LzNFBQol+ig0WjckIICSPwFZt0Cb0fCn29Bg8vg1u/hoTi48qESjdPR09nc+3kslQN8mHFbewJ8y2dGBXsMVH1gv83rZLPMluZAcxH5S0T+EZFeNtcCRCTWLB9YXAciMtKsE5uamlqyRrVbQ6+XjEyQ/0yz4xasJ7iSEQYpbu9xvv7XNZ6FGo3Gjcg8DqunwXsx8NVg2L8GOj8GozfCsFnQtAd4lfyVm5NXwANfrePo6Wxm3B5DrXIcvcYeL77i5pVFpww+QDOgK9AAWCUiEUqpdCBEKZUiIo2BX0Vks1LqP5s3SqnpwHSAmJgY+6YjMfcY+1A/Pw+NroT67e1qZiVD2hthkF5Zuo1rW9WmTtXy+0HSaMotKesNT7zN30NeJjS8ArqOg/D+4ON/UaKUUjy7YAv/7jnGu8PaEdkgyEFKlw3smUElAw1tXjcAUoqps1AplauU2g1sxzBYKKVSzL9JwG9Au1LqbCAC/d+FynVh7t2QddISsRengjB5YBty8wt4buEWp/evcX9KcjASkRARWSki60Vkk4hcb5ZfKyJxIrLZ/HuNTZvfTJmFzke1isrVOJjcLNgwC2ZcA9O7wpZ50Pa792eCAAAgAElEQVRmuP9PuGc5RN540cYJYOZfe/g2dj8PdWtKv7bul4Ld2dhjoNYCzUQkTET8gKFAUW+8BUA3ABEJxljySxKRaiLib1PeCUjAKgKrwQ0fGxuRPzxmHHhzMqHBFXm0R3NWJBxm2ZaDTu9f477YOBj1BsKBYSISXqTaeGCOUqodxth63yw/CvRTSrUB7gC+LNJuuFIqynw4bs9X81+O7YYVz8KbrYw98OxT0PtVeHwb9HvbOGB7ifyxI5VJPybQM7w2Y65tbqHSZZcSl/iUUnki8hCwHPAGPlVKxYvIRCBWKbXIvNZTRBKAfOBJpVSaiFwJfCQiBRjGcIqt958lhFwB3cbBr5OgSTdod6ul4u3h3i5hLN6YwnML4+nYJJiqgToMkgawcTACEJFCByPbMaCAKubzqpirE0qp9TZ14jH2cv2VUtkO11pTPKvegF9eBPGCln2MZIBhV1mSDHBX6mke/GYdzWtX5q2bo3TuORO7IkkopZYAS4qUPWfzXAFjzIdtnb+BS/9JYS+dxxihQpY8aXjL1Gzh8C5t8fX24pUbIhkw7U9eWbaNlwY5/pY1ZYLiHIwuL1LneWCFiDwMVAR6FCPnBmB9EeM0U0Tyge+BSeYY/A8iMhIYCRASEnKp96AB2PeP8SO4VV9jxlTFuuW3Exm5jPg8Fl9vL2bcHkNF/7IT4MfRlJ1IEhfCyxsGTTcOus2921gfdjJtGlTl7k5hfLNmH//uPub0/jVuiT0ORsOAz5RSDYDrgS9F5Oy4FJHWwCvAfTZthptLf13Mx23Fda6Umq6UilFKxdSs6fzzgh5D9imYfx9UbWhEfLDQOOXlF/DQrHXsP57Bh7e2p2H1CpbJ9gQ8w0ABVKkLAz+Ew1tgxXiXqDCmZ3MaVAtk3DwdBkkD2OdgdA8wB0AptRoIAIIBRKQBMB+43dbzVSl1wPx7CvgGYylR4yiWPwPH98Kgj4ywRBby0pJtrNp5lEkDI+gQpmN7FsVzDBRA857Q8SFYOwO2/uD07iv4+TB5UBt2pZ5hmg6DpLHPwWgf0B1ARFphGKhUEQkCfgTGKaX+KqwsIj6mwxEi4gv0BbQLqaPYvhTWfQ6dRkOjjpaK/nbtPj79azd3dQrl5sv0EmxxeJaBAug+AepGwcIHDe8+J3N185oMjKrHB78lsuPwKaf3r3EflFJ5QKGD0VYMb714EZkoIv3Nao8DI0RkIzALuNPcT3oIaAo8W8Sd3B9YLiKbgA3AAWCGc++snHA6FRY9DLXbQLenLRW9ds8xxi/YQpdmwTxzfStLZXsSUszeqkuJiYlRsbGxpROStgs+uspw+bzjB/B27qZj2ulserz5O2HBFZl7/5XaI6cMIyJxSqkYV+tRWiwZV+UJpWD2cEj8CUb+DrWLng64dPYfy2DgtL+oGujL/Ac6UbVC+fP6tXdced4MCqBGE+g7FfatNpJ/Obv7Sv6M7xPOun3pfLVmr9P712g0pWT9V7D9R2NFxkLjdCY7jxFfxJKTX8CMO2LKpXG6GDzTQIFxkjtqOPzxmuGC7mQGR9enS7NgXl22nYMnMp3ev0ajuUSO7YZlYyG0C1zxgGViCwoUj327gR2HTzHtlmia1KxkmWxPxXMNFBjnFWo0NVJznElzateFYZDyCgp4dkE87raUqtFoiqEgH+bfbxzGHfiBXcFd7eWtn3ewIuEw4/uEc1Vz7fZvD55toPwrwZBPISMNFoxyeiikkBoVeKxHc37eepilWw45tW+NRnMJ/PU27P8Hrn8dghqWXN9OFm1M4d1fE7k5piF3dQq1TK6n49kGCqBupJEgbOdy+OcDp3d/T+cwWterwoRF8ZzIyHV6/xqNxk4OboSVL0H4QIi8yTKxm5LTefK7jVwWWo0XB0aUy8SDl4rnGyiADiOgRR/46TkjNL4T8fH2YsrgSNJOZzNl2Van9q3RaOwkN8vYCqhQA/q+ZUl8PYDDJ7MY8UUswZX8+eDW9vj5lI+vXKsoH++WCAx4DyrVMkIhZTv3fFKbBlW5p3MYs/7dzz9Jzt0L02g0dvDLREjdBgOnlZjt1l6ycvMZ+WUcp7Ly+PiOGIIrXXz6jfJO+TBQYHzobvgYju+BH59wevePXduchtUDeXreZrJydRgkjcZtSPrNyMx92Qgj660FKKUY+/0mNu5P582bomhVt0rJjTTnUH4MFBiZd68eC5tmG8nGnEgFPx9eGtSGpKNnmLYy0al9azSa85CZDgseMLx9r51omdgPf09iwYYUnujZnF4RdSyTW94oXwYK4KonoFFn+PFxOOpcQ9GlWU0Gt6vPB7/tYvshHQZJo3E5S56EU4dg8HTwsyaS+M8Jh3l1+Tb6ta3Hg92aWiKzvFL+DJSXt/Fh9PGHuXdBnnPzv43vG06VQF/GzttEfoE+G6XRuIwt38PmOXD1U1C/vSUitx86xejZ64moV5VXb4jUHnulpPwZKICq9WHg+3BoE/w0waldV6/ox7N9W7F+Xzpf/aPDIGk0LuFkCvwwxjBMXR63ROSxMznc+8VaKvr7MOP2GAL9vC2RW56xy0CJSC8R2S4iiSIy9jx1bhKRBBGJF5FvbMrvEJGd5uMOqxQvNS16w+WjYM0HRkh9JzIwqj5XNa/Jq8u2kZKuwyBpNE6loMDYd8rPMRKdWhBMOievgFFfxXH4ZDbTb4+hTtUACxTVlGigRMQbmAb0BsKBYSISXqROM2Ac0Ekp1Rp41CyvDkzASHPdAZggItUsvYPScO0LUCfS+LCeOOC0bo0wSBEUKHhu4RYdBkmjcSZrP4akldBzEgSXfo9IKcXzi+NZs/sYr94QSVTDIAuU1IB9M6gOQKJSKkkplQPMBgYUqTMCmKaUOg6glDpill8H/KSUOmZe+wnoZY3qFuDjD0NmGvtQ80YacbicRMPqFRhzbXN+3nqEJZt1GCSNximk7oCfnoWm10LM3ZaI/PKfvXyzZh/3X92Ege3qWyJTY2CPgaoP2Gb+SzbLbGkONBeRv0TkHxHpdRFtEZGRIhIrIrGpqan2a28FwU2hzxuw90/443Wndn1Xp1Da1K+qwyBpNM4gPxfmjQDfCsbBfQscGP5KPMoLixPo0aoWT17XwgIlNbbYY6CK+y8WXZPyAZoBXYFhwMdmymp72qKUmq6UilFKxdSs6YIov1HDIHIo/D4F9vxVcn2L8PH24uXBbTiekcNLS3QYJI3Gofz+KhzcAP3ehsqlP5u0++gZHvh6HU1qVmTq0HZ468SklmOPgUoGbMP6NgBSiqmzUCmVq5TaDWzHMFj2tHUP+rwO1ULh+3sh45jTuo2oX5V7O4fxbex+Vu/SYZA0Goewfy2seh3a3gLh/Ust7mRWLvd+vhYvgY9vv4xK/s7N2l1esMdArQWaiUiYiPgBQ4FFReosALoBiEgwxpJfErAc6Cki1UzniJ5mmfvhX9nYjzqTCgsfdGpqjkd7NCekegWenq/DIHkiJXnBikiIiKwUkfUisklErre5Ns5st11ErrNXpsaG7NMwfyRUaQC9p5RaXH6B4uFv1rM3LYP3h7cnpIY1B3w151KigVJK5QEPYRiWrcAcpVS8iEwUkcKfIsuBNBFJAFYCTyql0pRSx4AXMYzcWmCiWeae1Isywp1sXwL/Tndat4F+3kweFMHuo2d499edTutX43js8YIFxmOMq3YYPwDfN9uGm69bYzgXvS8i3nbK1BSyYryRJXfQBxBQtdTipizdyu87UnlhQGs6NqlhgYKa82HXvFQptQRYUqTsOZvnChhjPoq2/RT4tHRqOpErRsHu340PdUhHI5+UE+jSrCaDo+vz0e9J9I2sp4NLeg5nvWABRKTQCzbBpo4CCv/hVfn/ZfABwGylVDawW0QSTXnYIVMDsGM5xM2EKx+B0M6lFvdd7H5mrNrN7R0bMfzyRhYoqLkQ5TOSxIUQgQHvG3lh5t5lLA84iWf7hFM10Jex8zbrMEiegz2erM8Dt4pIMsYPwYdLaOv+3rHuwJmjsPAhqNUarhlfanFxe4/zzPwtdGpag2f76gmrM9AGqjgq1oDBMyBtFyz9n9O6rVbRj+f6hbNxfzpfrN7jtH41DsUeT9ZhwGdKqQbA9cCXIuJ1gbZlwzvWlSgFi0dDVvr/x94sBdl5+Tz53UZqVfFn2i3R+Hrrr05noN/l8xHWBa56EjZ8DZvmOK3b/m3rcXXzmry2fDsHdBgkT8AeT9Z7gDkASqnVQAAQfIG2Zcc71lVs+Aa2/QDXPAt1IkotbvrvSSQdPcPkQW0IquBngYIae9AG6kJc/ZSxD/XDY8ZsygmICJMGRqAUPLtAh0HyAOzxgt0HdAcQkVYYBirVrDdURPxFJAzj6Ma/dsosvxzfA0ufMtLqdHyw1OL2pWXw3spE+rSpy9XNy9lM1MVoA3UhvH2MLLxePkaq+Lwcp3TbsHoFHu/ZnF+3HeGHTQed0qfGMdjpBfs4MEJENgKzgDuVQTzGzCoBWAY8qJTKP59M596Zm1KQD/NHGc8HfWCk1ykFSikmLNqCj5fofScXoE+XlUTVBjBgGnw7HH55Aa6b7JRu7+oUxqKNKbywOJ4uzYL1skIZxg4v2ASg03naTgbO+dAVJ1MDrH4P9v0NAz+AoJBSi1sef5iV21MZ36eVjlDuAvQMyh5a9YXLRhgf/h0rnNKlt5cwZXAkxzNydRgkjcYeDm2GX16EVv2g7bBSizuTncfExfG0rFOZO68MLb1+motGGyh76TkJakfAgvvhpHOW3cLrVWFEl8bMiU3m711HndKnRlMmyc2CefdBYDXo+7YlgWDf+WUnKSeymDQwAh/ttecS9LtuL74BRiik3EwjIrKTUnM82qMZjWpU4Ol5OgySRnNeVk6CI/HGcnzF0kd32H7oFJ/8uZubYhoQE1rdAgU1l4I2UBdDzeZw/WuwZxX8+aZTugzw9ealQW3Yk5bB27/oMEgazTnsXgV/v2fkd2res9TilFI8u2ALlQJ8GNu7lQUKai4VbaAulqjhEDEEVr4M+/5xSpedmgYzpH0Dpv+RRELKSaf0qdGUCbJOwIJRUD3MWIa3gO/XHeDfPccY26sl1Stq5yRXog3UxSICfd+CoIZOTc3xzPWtCAr0Zdy8TToMkkZTyNKn4GSKEfnFr2KpxaVn5PDykq1EhwRxU0zDkhtoHIo2UJdCQBUY8imcOgiLHnZKao6zYZCST/DZ33sc3p9G4/bEL4CNs+CqJ6BBjCUiX1u+neMZOUwa2AYvnYDQ5WgDdanUbw89njfCqcR+4pQu+7etR9cWNXljxXaSj2c4pU+Nxi05dQh+eBTqtTNCklnAhv3pfPPvPu68MozwejqbgDugDVRpuOJBaNoDlj0Nh7Y4vLvCMEgA43UYJE15RSkjqWhulrG05+1bapH5BYpn5m+mVmV/Hru2mQVKaqxAG6jS4OUFAz+EwCAjFFLOGYd32aBaBZ7o2YLftqeyaKOOD6oph8R+Aok/Q88XIdgaY/Ll6j3Ep5zk2b7hVA4ovcHTWIM2UKWlUk0jnP/RHcaGrRO448pQ2jYMYuLiBI6fcU58QI3GLTiaCMvHQ5PucNm9log8cjKLN1bsoEuzYPq0qWuJTI012GWgRKSXiGwXkUQRGVvM9TtFJFVENpiPe22u5duUe2bE5cZdocsYWP8lbJ7r8O6MMEhtOJGZy2QdBklTXsjPNQ7J+/gbB3ItiBYBMHnJVrLzCpg4IAKxSKbGGko0UCLiDUwDegPhwDARKS6s77dKqSjz8bFNeaZNef9i2nkGXcdBgw6w+FE4ttvh3bWqW4WRVzVmblwyfyXqMEiacsCqNyBlHfSbClWsmen8lXiUhRtSuL9rE8KCS++mrrEWe2ZQHYBEpVSSUioHmA0McKxaZRBvXzM1hxd8f49TUnM80r0ZoTUq8PR8HQZJ4+Ekx8Hvr0LkzdB6kCUis/PyeXbhFkKqV+CBrk0skamxFnsMVH1gv83rZLOsKDeIyCYRmSsitifcAkQkVkT+EZGBpVHW7anWCPq/Cwfi4NcXHd5dgK83Lw1uw960DKb+rMMgaTyUnDPG0l7lutD7VcvEfrxqN0mpZ3hhQGsCfEuXN0rjGOwxUMUtyhb1b14MhCqlIoGfgc9troUopWKAW4CpInLOTxURGWkasdjU1FQ7VXdTwgcYMcH+fsfwNHIwVzYJ5qaYBsxYlUR8ygmH96fROJ2fnoNju4wEhIFBlojcfyyDd37ZSe+IOnRrUcsSmRrrscdAJQO2M6IGwH/8m5VSaUqpbPPlDKC9zbUU828S8BvQrmgHSqnpSqkYpVRMzZoekFL5upegVjjMvx9OHXZ4d09f34pqFXwZN2+zDoOk8Sx2/gRrP4aOD0HYVZaINLLkxuOts+S6PfYYqLVAMxEJExE/YCjwH288EbHdseyPkYYaEakmIv7m82CMrKEJViju1vgGGqGQsk/D/JFQUODQ7oIq+DGhX2s2JZ9g5l+Od9DQaJxCxjHjQG7NVnDNs5aJ/SnhML9uO8JjPZpTLyjQMrka6ynRQCml8oCHgOUYhmeOUipeRCaKSKFX3iMiEi8iG4FHgDvN8lZArFm+Ephiprf2fGq1gt5TIOk3+Guqw7vrG1mXa1rW4o0VO9h/TIdB0pRxlILFow0jNXi6kY/NAjJy8nhhcQItalfmzk6hlsjUOA67zkEppZYopZorpZoopSabZc8ppRaZz8cppVorpdoqpboppbaZ5X8rpdqY5W2UUs4JWucuRN9heBz9Ogn2/+vQrkSEFwdG4CXwjA6D5DbYcYbwLZtzgjtEJN0s72ZTvkFEsgqdjETkMxHZbXMtytn35XA2fQtbF8E1z0DdSMvEvvNLIgfSM5k0KAJfnSXX7dH/IUciAv3ehqr1Ye49kJnu0O7qBwXyxHUt+GNHKgs36DBIrsaeM4RKqccKzwkC7wLzzPKVNuXXABnACpumT9qcL9zgjPtxGun7YMmTENIRrnzEMrE7D5/i41VJ3Ni+AZfpLLllAm2gHE1AVbjhUziVAosfcXhqjts7hhLVMIiJPyRw5FSWQ/vSlMjFniEcBswqpnwIsFQp5flrtwUFMH8UqAIY9CF4WeP+rZRi/IItVPT3YWzvlpbI1DgebaCcQcPL4JrxkLAQ4j5zaFfeXsKrQyLJyMnjoW/Wk5vvWAcNzQWx9wwhItIICAN+LebyUM41XJPNc4dvFToiFSOz7B3f+Gca7P0Ter8C1UItEzt//QHW7D7GU71aUqNSsW+Xxg3RBspZXDkaGneDZWPhsGP9RJrXrszLg9vw7+5jvLJ0m0P70lwQe84QFjIUmKuU+k9IENNDtg2Gk1Ih44CWwGVAdaDYKMVl7vjG4Xj4ZSK07AtRwy0TeyIjl5eWbCWqYRBDL9NZcssS2kA5Cy8vGPQR+Fc2U3M4drVmULsG3NGxER//uZvFOi2HqyjxDKENxc2SAG4C5iulcgsLlFIHlUE2MBNjKbFsk5cN80YaS+L93rYsECzAayu2cexMDpMGRugsuWUMbaCcSeXahpFK3QrLxzm8u2f6hNO+UTWe+n4TOw6fcnh/mnMo8QwhgIi0AKoBq4uRcc6+VOG5QzFCbw8EHJ8t09GsnAyHt0D/96BisGViN+5P5+s1+7jjylAi6le1TK7GOWgD5WyadodOo429qPj5Du3Kz8eL94dHU8HPh/u/jONUVm7JjTSWYecZQjCM0GxV5GyAiIRizMB+LyL6axHZDGwGgoFJjrkDJ7HnL/jrHeNYRotelonNLzAcI2pW8mfMtc0tk6txHj6uVqBccs2zxqBcNBrqRRtBZh1E7SoBvHdLO4Z/vIYnvtvIh7e21zlvnIhSagmwpEjZc0VeP3+etnsoxqlCKXWNdRq6mKyTRkiwao2MEGEW8vWavWw+cIJ3hrXTWXLLKHoG5Qq8fWHIJ4AyUnPkO3Zmc0XjGozr3ZLl8Yf56I8kh/al0VwUy8bCyWQYNB38K1km9sipLF5bvp3OTYPpF6mz5JZVtIFyFdVCjcRryWuN9XcHc0/nMPq0qcury7bxt05wqHEHti6GDV9D5zEQcrmlol9eso3s3AImDmitVwzKMNpAuZKIGyD6dvhzKuwq7viLdYgIrwyJpHHNSjw0az0p6ZkO7U+juSCnDhux9uq2hauL9ZK/ZP7edZT56w9w39WNaVzTulmZxvloA+Vqer0Cwc1h3n1w+ohDu6rk78OHt7YnJ6+AUV+vIztPZ+HVuAClYNHDRiLCQdPBx88y0Tl5BTy7YAsNqwfyYLemlsnVuAZtoFyNXwW4cSZkm5vFDk7N0bRWJV6/MZKN+9OZuLh8BJbXuBlxn8HO5dDjBahlbdihj/9MYlfqGSb2j9BZcj0AbaDcgdqtDQ+mXb/A6ncd3l2viLrcd3Vjvl6zj+9i95fcQKOxirRdsPxpaNwVOoy0VHRhltzrWtemW0udJdcT0AbKXYi5G1r1N0K9JMc5vLsne7agY+MajF+whS0HdKp4jRPIz4P59xlerAPeN6KrWMgLixPwEmFCv9aWytW4Dm2g3AUR6P8OVK4Lc++CLMcaDR9vL969pR3VK/ox6us40jNyHNqfRsOfbxleq33eNFLQWMhPCYf5eethRndvprPkehDaQLkTgdXghk/gRDIsftThqTmCK/nz/vBoDp3IYvTsDRQU6CSHGgdxYB38PgUihkCbIZaKzsjJ4/lF8TSvXYm7O4dZKlvjWrSBcjdCLoduT0P8PFj/pcO7axdSjQn9WvP7jlTe/mWnw/vTlENyMoylvYq1oM/rlot/71czS+7ANjpLrodh13/TjrTVd4pIqk0K6nttrt0hIjvNxx1WKu+xdH4Mwq6GJf+D1O0O72745SHcEN2At3/Zya/bDju8P0054+fn4egOGPi+sUpgIYlHTjFjVRI3RDegQ5jOkutplGig7ElbbfKtTQrqj8221YEJwOUYKQEmiIi1n1BPxMsbBk8Hv4rw3V2Q69hDtSLC5EERhNetwqOzN7AvzfMTt2qcROIv8O9HcPkoaNLNUtGFWXIr+Pnw9PU6S64nYs8M6mLTVttyHfCTUuqYUuo48BNgXbhiT6ZyHSPl9ZF4WDHe4d0F+HqfDSR731dxZOboQ7yaUpJxDBY+CMEtoMcEy8Uv3JDCP0nH+F+vFjpLrodij4GyN231DWYK6rkiUpikza62ZTI1tTNodi10fAjWfgwJ56QRspyQGhWYOjSKbYdO8syCzSgHO2loPBil4McxcCbVWA3wtdaz7kRmLpN+TKBtwyCGXRZiqWyN+2CPgbInbfViIFQpFQn8DHx+EW3LXmpqZ9J9AtRrB4segvR9Du+uW4tajO7ejHnrDvDVGsf3p/FQNs818p11HQf1oiwX/8aK7Rw7k8NknSXXo7HHQJWYtloplWamnwaYAbS3t62mBHz8YMinRgik70cYhx0dzCPXNKNbi5pMXBzPun3HHd6fxsM4kQw/Pg4NL4dOj1oufnPyCb78Zy+3d9RZcj0dewxUiWmrC1NQm/THyB4KRibRniJSzXSO6GmWaS6G6o2N1Bz7/zHOkjgYLy/hrZujqFM1gAe+WsfR09klN9JowPghtWAUFOQZe6je1uZEzS9QPLNgM8GV/BnTU2fJ9XRKNFB2pq1+RETiRWQj8Ahwp9n2GPAihpFbC0w0yzQXS5shEHUr/PE6JBXNAG49QRX8+GB4e45n5PDwN+vJy3dsEFuNh7DmQ9j9B/R62fhhZTHf/LuPTcknGN+nFVV0llyPx65zUEqpJUqp5kqpJkqpyWbZc0qpRebzcUqp1kqptkqpbkqpbTZtP1VKNTUfMx1zG+WE61+FGk1h3kg44/ikgxH1qzJ5UBtWJ6Xx2nLHn8fyROw4Q/iWzfnBHSKSbnMt3+baIpvyMBFZY54t/NZc2XA9R7YaZ56a9zbynFlM6qlsXl22jSub1KB/23qWy9e4H/rYdVnCr6KRmiPzuLmM4vhZzZD2Dbj1ihA++iOJpZsPOrw/T8KeM4RKqccKzw8C7wLzbC5n2pwt7G9T/grwllKqGXAcuMehN2IPeTkwbwT4VzZiSjogi+3LS7aSlZvPxAEROktuOUEbqLJGnTZw3WTYuQLWfOCULp/tG05UwyCe+G4jiUdOO6VPD+FizxAOA2ZdSKAY38zXAHPNos+BgRboWjp+exkObTaMUyXrU138k5TGvPUHuO+qJjStpbPklhe0gSqLXHYvtOwLP02AlPUO787fx5sPbo0mwNeb+7+K43S24z0JPQR7zxAiIo2AMOBXm+IA83zgPyJSaIRqAOnm3nBJMp1zvnDvavhrKrS7DVr2sVx8YZbcBtV0ltzyhjZQZRER6P8uVKpthELKOunwLutWDeTdYe1ISj3NU3M36UO89mHXOUCTocBcpZRtCI8QpVQMcAswVUSaXIxMp5wvzD5lBIKt2tBwjHAAn/y5m51HTvNC/9YE+uksueUJbaDKKhWqww0zIH2vcebECQbjyqbB/K9XS37cfJCPV+12eH8ewMWcAxxKkeU9pVSK+TcJ+A1oBxwFgkSk0H/btWcLl42DE/uNaBH+lS0Xn3zcyJLbM7w23VvVtly+xr3RBqos0+hK46T+5jmw8YJbF5Zx31WN6dW6DlOWbWP1rjSn9FmGKfEMIYCItACqAattyqqJiL/5PBjoBCQoY+q6EihMqnQHsNChd3E+tv1opITp9CiEXOGQLiYuTgBgQn+dJbc8og1UWafL4xDaxZhFHXV8PicR4bUbI2lUowIPz1rHoRNZDu+zrGLnGUIwnCNmq/+um7YCYs2zhSuBKUqpBPPaU8AYEUnE2JP6xNH3cg6nj8CiRwynna7jHNLFL1sPsyLhMKN7NKO+zpJbLhF320uIiYlRsbGxrlajbHEyBT7oBFXqw70/g2+Aw7vcefgUA6b9Rcs6lZk9siN+Pp75W0dE4sx9oDKNpeNKKZg1DHb9CiN/g9rFZeIxZ9MAABgmSURBVN8pHZk5+Vz71u8E+nrz4yNdPPbzVV6xd1zp/7onUKUeDPwADm+Gn55zSpfNalfm1SGRrNuXzuQfE0puoPEc1n0BO5YaKTQcYJwApq1MJPl4Ji8OjNDGqRyj//OeQotecMUDRnK4bT86pcu+kfW4t3MYn6/ey/z1yU7pU+NijiUZjhGhXYwkhA4g8chpPvpjF4Oj63NF4xoO6UNTNtAGypPo8TzUiTSSxJ1wjsEY27sll4dVZ9y8zSSkON7dXeNC8vNg3n3g5WPM2L2s//pQSvHcwi0E+nozrncry+VryhbaQHkSPv5w42eQnwtz73FK/igfby/euyWaqoG+3P9VHCcych3ep8ZF/DUVkv+FPq9DUMOS618Cizam8PeuNJ7s1ZKalXWW3PKONlCeRo0m0O9t44vk7bbGZnbizw6N21ezsj/vD48mJT2TMXM2UFDgXo43Ggs4uNEIZ9R6ELS50SFdnMzKZdKPW2nboCq3dNBZcjXaQHkmbYbA6E3Q+THY/y98dQO81x7+fg8yHJPtpH2j6jzbN5xfth1h2sr/a+/Ow6OosgYO/04SEgyQEFZZwxbWMCQBEVQcRARljYw6oIjLiIIKOriNyIAizKifgopiZhw3XBAFZRNUFJFhFISwSNhCQGQR2QlrYkju90d1sMVAV0h3VyV93ufpx+7qqq7TZV9Oquree7ICsg/lkLyT1gz60dWg54SATAQLMOHzTPYfy2VcamvCtUquQhNU2VW5Hlw5Gkashz+9BhVqwOePwYQW1j2qAMzhN6hjPKlJtZnwRSZfZwZw7jcVXF+OhX0bIfVlawaTAMjYlc2Ub7dxc4d4WtfVKrnKogmqrIuIss6o/vIZDFkCbfpDxkfw787wahdYPRXy/DPYVkT4R7/WNKtZifveX8WOgyf88rnKQVsXwdLJ0P5OaNI1ILuwquRmUKVCFA90axaQfajSSRNUKLmwtXV/6oGNcM0z1kSfM4dYZ1ULRsOhbSXeRXRkBGkD25JfYBj6bjo5efm+N1LudPIQzLwbqiZA1ycCtpv3l29nzY7DjOrZgtgLtEqu+pWtBOWrKqjXeteJiBGRdp7XDUTkpFdV0DR/Ba5KoHwsXHwX3PMdDJoNDS617k+9kATvXg+Zn0PB+SeWBtUqMPGGJDJ2HeHvMzN05vPSat5DcGyPNRFsZHRAdrH/WC5Pz99Ix0ZV6ZukVXLVb0X4WsGrKuhVWLMzLxeR2V7zghWuVwkYDiw74yO2eKqFKrcRgUZ/tB7Zu2DlW5D+Jrx3PVSOh4v+AkkDoULxB0t2bVmTYV2aMGlhFinxcQzQXlmly9rpsPZDuOIxqJMSsN38c95GTubl82RqK62Sq37HzhmU3aqgTwLPADp7aGkUWweuGAn3Z8B1b0BsXeuy34QW8PEQ2Jle7JIe93dtSqeEaoyZtY41Ow4HKHDld0d+gk9GQJ12cNmIgO1m2dYDzFi5k8GdGtGkhv9LdajSz06C8lkVVESSgXrGmLlFbN9QRFaJyNci0qmoHQSt8qfyLSISEvvBbfNg6LeQcjNsmAP/6WJ1rFj1jtXt2IbwMOHF/slUrxTF0HfSOXj8l8DGrkquoMC675SfZ13aC/d5keW85OUX8PdZGdSpfAHDuiQEZB+q9LOToM5ZwVNEwoCJwANFrLcbqypoMjACeE9EYn73YcGo/KmKr2ZL6PkcjNgAPZ6FUzlWF/XnmsNnj8GBLT4/Iq5CJGkD27L/+C8Mn7qKfB3E627LX4WtX0H38dag7wB5fckPZO7RKrnq3OwkKF9VQSsBicAiEdkGdABmi0g7Y0yuMeYAgDEmHdgCNPVH4CqIysdA+8Fw91K49RNofAUsS4NJKfB2P9g0/5ydKlrXjWVc30SWZO3nuc83BTFwVSz7NlmXdRO6QdvbArabnw6f5PkvNtO1RU26ttQquers7Jy/n64KCuzCqgp6Y+GbxphsoFrhaxFZBDxojFkhItWBg8aYfBFpBCQAW/0YvwomEWhwmfU4stsqu5D+BkztD7H1od1tkDIIKlT73aY3XFSPVTsOMXnRFtrUq0z3Vhc68AXUWZ36BT4aDOWioc9LAZstAuCJOeswGB7vE5hSHars8HkGVYyqoEW5HPjeUxV0OjDEGBOYuXZUcMXUgs6PwP1r4YYpEBcPXz5hdaqYMdiaYumMThVjerfiD3VjefCDNWzdd8yhwFWRFj9jzbfX+wWoFLizmoUb9/DZuj0MvzKBunGB6bquyg6tqKv8Z98mWP4arJkKuUesgcEXDbZmsoisAMDOQyfoPWkJ1StFMfOeS4mODMxNeH8JiYq6O76D17tDmwGQOjlgMeTkWVVyoyLCmadVckOaVtRVwVe9GfR4xupU0Wui1SNsznB4rgXM/xvsz6JuXDSTBqSQtfcYj8xYW+YH8foa5C4iE70GsmeKyGHP8iQR+VZE1onI9yLyZ69t3hSRH7y2O/9xhrnHrIlgY+rC1U+d98fY8fJXWew4eJIn+2qVXGWPu/98VaVTVEVod7t1o337Ulj+H+ux7BVo1JnLLhrMg1c14ZnPt5BcrzK3X9bQ6YgDws4gd2PMX73WHwYke16eAAYZYzaLSG0gXUQ+M8YUDih7yBgzvcRB/nLcmlj4j3+zOsMEyJZ9x/jX11u5NrkOHRtrlVxljyYoFTgiEN/Rehz9B6yaAivegGk3MTSmDrVrdeOf87JJrBNL+4aBmSXbYacHuQOISOEg9/VnWX8AMAbAGJNZuNAY85OI7AWqA/4d8VyppjXdVQA7RRhjGDNrHVHlwhjZQ6vkKvv0PFsFR6WacPlDVp2qP7+LVGtK6qE3WBI5jENTBnJo/aJiz1RRCvgc5F5IROKBhsDCIt5rD0RiDdMoNN5z6W+iiBRZetb2APgATzE05/vdLMnaz8Pdm2mVXFUsmqBUcIVHQIteMGgm3LuCo61voWPBKuI+6It55RKrk0Vumenhd85B7mfoD0w3xvxmQJmI1ALeBm4zxhSWRX4UaA5cBFQBHinqA90wAP5ITh5Pzl1P6zqx3HhxvCMxqNJLE5RyTrUEqvxpAot7L+aRvMH8fDTPmgPuuebWTNp7NzodYUn5GuTurT8w1XuBZ9aVT4BRxpilhcuNMbuNJRd4A+tSoitNXGBVyR1/baJWyVXFpglKOa5XuwSiO9xGx0OPs/jy96B5D2tW9ckXw5u9YN1Ma2640uf0IHcRicRKQrPPXElEmgFxwLdeyyKBj4EpxpgPz1i/lue/AqQCGQH7BiWQsSubt77ZxsCL4/lD3cpOh6NKIU1QyhVG9mhBu/gq3PVVOJsu8cz/d+UYOPQjfHgLPN8aFj1lzWBRShRjkPsA4H3z2z73N2ANdL+1iO7k74rIWmAt1iwu4wL+ZYqpoMAwamYGVSpE8qBWyVXnSQfqKtfYeySHnpOWUDEqgln3XkpM+XLWHH+bP7e6qWd9AWER0LwXXHSHNeVSgG/wh8RA3QCY+t12Hv1oLRNuaEO/lLpB268qHXSgrip1asSU5+UbU9h+8AQPfLCGggIDYeHQ7BoYOAOGrYSLh8DWRfBWL5jcAb57FXKOOB268nLgWC5Pzd/IxQ2rcG1ykZ0WlbJFE5RylfYNqzCyRwsWrN9D2uIzynlUbWyVgRixAfq+DBHlYd6D1vx/c0fAnrMNL1LB9NT8jRzPPcW41EStkqtKRBOUcp3bL21A7za1efazTSzZvP/3K0RGQ/JAuOtruGMhtOhjFVJ8pSO80QMyZlizc6ugW77tIB+m72Tw5Y1IqKlVclXJaIJSriMiPNWvNU1qVGTY1JXsOnyOCr5128K1r1hnVVeNheydMP12eD4RFo6H7F3BCzzE5eUXMOrjwiq5TZwOR5UBmqCUK1WIiiBtYFvy8g1D30knJ+/sBRGtDarCpffB8NVw44dQKwkW/5/V+2/aQOu+lcs6BJU1b/5vG5v2HGVM75aun6VelQ6aoJRrNapekWevb8P3O7N5Yo7N+0thYdC0G9z0Ady3Gi65F7b9D6b0hZcugqVpcNK/09kp2J19kolfZNK1RQ26aTFK5SeaoJSrXZ14IUM7N2bqd9v5YPkO3xt4i2tgXfYbsQFS06B8LHz6iNWpYs598PPagMQcisbOWU+BMYzp3crpUFQZoglKud6D3ZpxWZNqjJqVQcau7OJ/QLnykDQABn8Jdy6CxH6w5n1Iuwxe6w4b5vg75JDy1aa9zM/4mWFdEqhXRavkKv/RBKVcLzxMeKF/EtUqRHLX2+kcOl6CHnq1k60u6iM2QLfxcHyvVbNKnZecvHzGzFpH4+oVGNypkdPhqDLGVoLyVRXUa73rRMSISDuvZY96ttskIt39EbQKPVUrRjF5YFv2Hc3lvmmryS8oYYeH6CrW/al70+GKkf4JMgRNXrSF7QdPaJVcFRA+f1FeVUGvAVoCA0SkZRHrVQKGA8u8lrXEmiCzFXA1MNnzeUoVW1K9yjzepxWLM/fxwheZvjewIywMIiv457NCzA/7j5O2aAt9k2pzSZNqToejyiA7f/KcrgpqjPkFKKwKeqYngWeAHK9lfbEmwcw1xvwAZOHi0gDK/Qa0r8f1bevy4sIsvtywx+lwQpYxhtGzMoiKCOOxnlolVwWGnQTlsyqoiCQD9Ywxc4u7rWd7e5U/VcgTEZ5MTSSxTgz3T1vNtv3HnQ4pJH2ydjf/3byfB7s3o0al8k6Ho8ooOwnqnFVBRSQMmAg8UNxtTy9wQeVPVXqULxfOKze1JTxMGPJOOid/8TGIV/nV0Zw8xs5ZT2KdGAZ20Cq5KnDsJChfVUErAYnAIhHZBnQAZns6ShSnoqhSttWrEs3zf05i056jjPx4LW4rG1OWTVywmX3HchmX2lqr5KqAspOgzlkV1BiTbYypZoxpYIxpACwF+hhjVnjW6y8iUSLSEEgAvvP7t1AhqXOzGvy1a1M+XrWLt5f+6HQ4IWHdT9m8+c0P3Ni+Pkn1tEquCiyfCaoYVUGL2nYd8AGwHvgUuMcYo9djlN/ce0UTujSvwdg560n/8aDT4ZRpBQWGv8/MIC46koe7N3c6HBUCbA1cMMbMM8Y0NcY0NsaM9ywbbYyZXcS6nT1nT4Wvx3u2a2aMme+/0JWCsDBh4g1J1K58AXe/u5J9R3OdDqnM+mDFDlZuP8zIHi2IjS7ndDgqBOjIOlXqxUaXI21gW7JP5nHveys5lV/gdEin+RrkLiITRWS155EpIoe93rtFRDZ7Hrd4LW8rIms9n/miBKEq4MHjv/DUpxtp37AK/VK0Sq4KDk1QqkxoWTuGf/ZrzbIfDvL0pxudDgewN8jdGPNXY0ySMSYJmAR85Nm2CjAGuBhr7OAYEYnzbPYKcCfWPd0ErEHwAfX0/I0cy9EquSq4NEGpMuPa5LoM6hjPq//9gU++3+10OGB/kHuhAcBUz/PuwAJjzEFjzCFgAXC1iNQCYowx3xqr6+IUIDVwXwFWbDvItBU7+EunhjTVKrkqiDRBqTJlVM+WJNevzEPT15C196jT4dgaqA4gIvFAQ2Chj23reJ77/Ex/OJVfwKiZGdSOLc/wLgmB2o1SRdIEpcqUyIgwXrmpLdGR4dz5djpHc/KcDMfWQHWP/sB0r16uZ9vW9mf6Y4aWN7/ZxsafjzKmTysqRGmVXBVcmqBUmXNhbHkmDUjhxwMneHj6904O4i3OQPX+/Hp571zb7vQ89/mZJZ2hZXf2SSYuyKRL8xp0a1mz2NsrVVKaoFSZ1LFxVf52dXPmZ/zMvxdvdSqMcw5yLyQizYA44FuvxZ8B3UQkztM5ohvwmTFmN3BURDp4eu8NAmYFIvhxczdwqsDweO9W2jFCOUITlCqz7ujUkB6tL+TpTzfyTdb+oO+/GIPcB2DN+m+8tj2IVSFguecx1rMMYCjwH6zqAFsAv48v/DpzH5+s3c2wLk2oX1Wr5CpniNvmMGvXrp1ZsWKF7xWVsuFY7in6vrSEwyfymDv8MmrFXlCs7UUk3RjTzvea7lacdpWTl8/Vzy8mTIT593ciKkJLuCn/stuu9AxKlWkVoyL4183tyMnLZ+g7K8k9pTNt+ZL29Ra2HTjBk6mJmpyUozRBqTKvSY2KPHt9G1bvOMy4uRucDsfVtu0/zuRFW+jTpjaXapVc5TBNUCokXNO6Fndd3oi3l/7IjPSdvjcIQcYYRs9eR1R4GKO0Sq5yAU1QKmQ81L0ZHRpVYeTHa1n3U7bT4bjO/IyfWZy5jwe6NaVGjFbJVc7TBKVCRkR4GJMGpBAXHcmQd9LJPuHoIF5XOZZ7irFz1tOqtlbJVe6hCUqFlOqVonj5phR+zs7h/mmrKChwVy9Wpzy/IJM9R3MYl5pIRLj+s6DcQX+JKuS0jY9jdK+WfLVpH5MWZjkdjuM27D7CG99sY0D7+iTXj/O9gVJBoglKhaSBHeLpl1KH57/M5KtNe50OxzEFBYZRMzOIvaAcD3dv5nQ4Sv2GrQRlo+jaEE8BtdUisqSw5o2INBCRk14F2dL8/QWUOh8iwvjU1jS/MIaFG0I3Qe06fJLtB0/w6DXNqRwd6XQ4Sv2Gz+mJvYquXYU1UeVyEZltjFnvtdp7xpg0z/p9gAn8WkRti6cYm1KuckFkOO/f2YGY8qE7S3e9KtEsfOCPVNSZypUL2TmD8ll0zRhzxOtlBc5eUkApV4m9oFzIT4RaqbweA+VOdhKUraJrInKPiGwBngGGe73VUERWicjXItKpqB34o26NUkqpssVOgrJVIM0Y87IxpjHwCDDKs3g3UN8YkwyMAN4TkZgiti1R3RqllFJlj50EVZyia2BdAkwFMMbkGmMOeJ6nY5UGaHp+oSqllAoldhKUz6JrIpLg9bInsNmzvLqnkwUi0ghIAByrHqeUUqr08Nl1xxhzSkQKi66FA68XFl0DVhhjZgP3ikhXIA84BNzi2fxyYKyInALygSFeRdeUUkqps7LVt9QYMw+Yd8ay0V7P7zvLdjOAGSUJUCmlVGjSmSSUUkq5kiYopZRSriTGuGtMrYjsA348xyrVgP1BCscujck+N8Z1rpjijTGlfuyDtiu/0ZjsK3G7cl2C8kVEVhhj2jkdhzeNyT43xuXGmILNjcdAY7LHjTGBf+LSS3xKKaVcSROUUkopVyqNCerfTgdQBI3JPjfG5caYgs2Nx0BjsseNMYEf4ip196CUUkqFhtJ4BqWUUioEaIJSSinlSq5NUDbKzEeJyDTP+8tEpIELYrpVRPZ5lbi/IwgxvS4ie0Uk4yzvi4i86In5exFJcUFMnUUk2+s4jS5qPT/HVE9EvhKRDSKyTkR+Nz2XE8cqmNzYpmzGFdR25cY2ZTOuoLaroLQpY4zrHliT0m4BGgGRwBqg5Rnr3A2keZ73B6a5IKZbgZeCfKwuB1KAjLO83wOYj1XXqwOwzAUxdQbmBvk41QJSPM8rAZlF/P8L+rEK4vd3XZsqRlxBbVdubFM24wpquwpGm3LrGZTPMvOe1295nk8HrhQJaN1qOzEFnTFmMXCuGeL7AlOMZSlQWURqORxT0BljdhtjVnqeHwU28PvK0EE/VkHkxjZlN66gcmObshlXUAWjTbk1QdkpM396HWPMKSAbqOpwTAB/8pzKTheRekW8H2x24w62jiKyRkTmi0irYO7Yc+kqGVh2xltuPVb+4MY2ZTcucFe7cvPvxJF2Fag25dYEZafMvK1S9H5kZ39zgAbGmD8AX/DrX6NOCvZxsmMl1lxcbYBJwMxg7VhEKmKVgLnfGHPkzLeL2MTpY+UvbmxTdvfptnbl1t+JI+0qkG3KrQnKTpn50+uISAQQS2BPf33GZIw5YIzJ9bx8FWgbwHjssnMsg8oYc8QYc8zzfB5QTkSqBXq/IlIOqyG9a4z5qIhVXHes/MiNbcpWXC5sV678nTjRrgLdptyaoHyWmfe8Lqzcex2w0HjuyjkV0xnXVvtgXZN12mxgkKc3TQcg2xiz28mAROTCwnsbItIe63d4IMD7FOA1YIMxZsJZVnPdsfIjN7YpW3G5sF258ncS7HYVjDZlq6JusBl7ZeZfA94WkSysv/L6uyCm4SLSBzjlienWQMYEICJTsXrvVBORncAYoJwn5jSsSsg9gCzgBHCbC2K6DhgqIqeAk0D/IPxDeClwM7BWRFZ7lo0E6nvFFfRjFSxubFPFiCuo7cqNbcpmXMFuVwFvUzrVkVJKKVdy6yU+pZRSIU4TlFJKKVfSBKWUUsqVNEEppZRyJU1QSimlXEkTlFJKKVfSBKWUUsqV/h+yrwyKxdMUGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c09509ca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 16s 6ms/step\n",
      "\n",
      "Accurancy: 0.874\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 3\n",
    "\n",
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "x = Bidirectional(LSTM(units=lstm_units, return_sequences=True))(x)\n",
    "x = Bidirectional(LSTM(units=lstm_units))(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_token_train, y_token_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_token_test, y_token_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.3f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('word_saved_models/BiLSTM-Token-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BiLSTM - Morph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/3\n",
      "8195/8195 [==============================] - 217s 26ms/step - loss: 0.7777 - acc: 0.6633 - val_loss: 0.6716 - val_acc: 0.7052\n",
      "Epoch 2/3\n",
      "8195/8195 [==============================] - 210s 26ms/step - loss: 0.5638 - acc: 0.7790 - val_loss: 0.4487 - val_acc: 0.8350\n",
      "Epoch 3/3\n",
      "8195/8195 [==============================] - 211s 26ms/step - loss: 0.3663 - acc: 0.8722 - val_loss: 0.3792 - val_acc: 0.8638\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c08f0a3da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 16s 6ms/step\n",
      "\n",
      "Accurancy: 0.875\n"
     ]
    }
   ],
   "source": [
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "x = Bidirectional(LSTM(units=lstm_units, return_sequences=True))(x)\n",
    "x = Bidirectional(LSTM(units=lstm_units))(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_morph_train, y_morph_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_morph_test, y_morph_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.3f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('word_saved_models/BiLSTM-Morph-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP - Token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/6\n",
      "8195/8195 [==============================] - 56s 7ms/step - loss: 0.8829 - acc: 0.5907 - val_loss: 0.7232 - val_acc: 0.6950\n",
      "Epoch 2/6\n",
      "8195/8195 [==============================] - 55s 7ms/step - loss: 0.7718 - acc: 0.6595 - val_loss: 0.6982 - val_acc: 0.7135\n",
      "Epoch 3/6\n",
      "8195/8195 [==============================] - 56s 7ms/step - loss: 0.7250 - acc: 0.6948 - val_loss: 0.6528 - val_acc: 0.7243\n",
      "Epoch 4/6\n",
      "8195/8195 [==============================] - 55s 7ms/step - loss: 0.6569 - acc: 0.7300 - val_loss: 0.5763 - val_acc: 0.7672\n",
      "Epoch 5/6\n",
      "8195/8195 [==============================] - 56s 7ms/step - loss: 0.5388 - acc: 0.7971 - val_loss: 0.4485 - val_acc: 0.8321\n",
      "Epoch 6/6\n",
      "8195/8195 [==============================] - 56s 7ms/step - loss: 0.4014 - acc: 0.8664 - val_loss: 0.3789 - val_acc: 0.8638\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c09c4b2a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 2s 702us/step\n",
      "\n",
      "Accurancy: 0.868\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 6\n",
    "\n",
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "x = Flatten()(x)\n",
    "x = Dense(256, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "x = Dense(64, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_token_train, y_token_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_token_test, y_token_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.3f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('word_saved_models/MLP-Token-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP - Morph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8195 samples, validate on 2049 samples\n",
      "Epoch 1/6\n",
      "8195/8195 [==============================] - 57s 7ms/step - loss: 0.8336 - acc: 0.6349 - val_loss: 0.7146 - val_acc: 0.6940\n",
      "Epoch 2/6\n",
      "8195/8195 [==============================] - 56s 7ms/step - loss: 0.7561 - acc: 0.6727 - val_loss: 0.6825 - val_acc: 0.7101\n",
      "Epoch 3/6\n",
      "8195/8195 [==============================] - 56s 7ms/step - loss: 0.7124 - acc: 0.6981 - val_loss: 0.6535 - val_acc: 0.7194\n",
      "Epoch 4/6\n",
      "8195/8195 [==============================] - 56s 7ms/step - loss: 0.6369 - acc: 0.7389 - val_loss: 0.5678 - val_acc: 0.7579\n",
      "Epoch 5/6\n",
      "8195/8195 [==============================] - 55s 7ms/step - loss: 0.5140 - acc: 0.8088 - val_loss: 0.4434 - val_acc: 0.8253\n",
      "Epoch 6/6\n",
      "8195/8195 [==============================] - 56s 7ms/step - loss: 0.3832 - acc: 0.8663 - val_loss: 0.3724 - val_acc: 0.8580\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c09d746ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2560/2560 [==============================] - 2s 717us/step\n",
      "\n",
      "Accurancy: 0.864\n"
     ]
    }
   ],
   "source": [
    "# Create new TF graph\n",
    "K.clear_session()\n",
    "\n",
    "# Construct model\n",
    "text_input = Input(shape=(max_document_length,))\n",
    "x = Embedding(vocabulary_size, embedding_size)(text_input)\n",
    "x = Flatten()(x)\n",
    "x = Dense(256, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "x = Dense(64, activation='relu')(x)\n",
    "x = Dropout(dropout_keep_prob)(x)\n",
    "preds = Dense(3, activation='softmax')(x)\n",
    "\n",
    "model = Model(text_input, preds)\n",
    "\n",
    "adam = optimizers.Adam(lr=lr)\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=adam,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(x_morph_train, y_morph_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=num_epochs,\n",
    "                    verbose=1,\n",
    "                    validation_split=dev_size)\n",
    "\n",
    "# Plot training accuracy and loss\n",
    "plot_loss_and_accuracy(history)\n",
    "\n",
    "# Evaluate the model\n",
    "scores = model.evaluate(x_morph_test, y_morph_test,\n",
    "                       batch_size=batch_size, verbose=1)\n",
    "print('\\nAccurancy: {:.3f}'.format(scores[1]))\n",
    "\n",
    "# Save the model\n",
    "model.save('word_saved_models/MLP-Morph-{:.3f}.h5'.format((scores[1] * 100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
